Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate

LOF of logistic GEE models and cost efficient Bayesian optimal designs for nonlinear combinations of parameters in nonlinear regression models

Doctor of PhilosophyDepartment of StatisticsShie-Shien YangWhen the primary research interest is in the marginal dependence between the response and the covariates, logistic GEE (Generalized Estimating Equation) models are often used to analyze clustered binary data. Relative to ordinary logistic regression, very little work has been done to assess the lack of fit of a logistic GEE model. A new method addressing the LOF of a logistic GEE model was proposed. Simulation results indicate the proposed method performs better than or as well as other currently available LOF methods for logistic GEE models. A SAS macro was developed to implement the proposed method. Nonlinear regression models are widely used in medical science. Before the models can be fit and parameters interpreted, researchers need to decide which design points in a prespecified design space should be included in the experiment. Careful choices at this stage will lead to efficient usage of limited resources. We proposed a cost efficient Bayesian optimal design method for nonlinear combinations of parameters in a nonlinear model with quantitative predictors. An R package was developed to implement the proposed method

Inequalities about normalized Lp projection body

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Analysis of Differentiated Chemical Components between Zijuan Purple Tea and Yunkang Green Tea by UHPLC-Orbitrap-MS/MS Combined with Chemometrics

Zijuan tea (Camellia sinensis var. assamica cv. Zijuan) is a unique purple tea. Recently, purple tea has drawn much attention for its special flavor and health benefits. However, the characteristic compounds of purple tea compared with green tea have not been reported yet. The present study employed a non-targeted metabolomics approach based on ultra-high performance liquid chromatography (UHPLC)-Orbitrap-tandem mass spectrometry (MS/MS) for comprehensive analysis of characteristic metabolites between Zijuan purple tea (ZJT) and Yunkang green tea (YKT). Partial least squares-discriminant analysis (PLS-DA) indicated that there are significant differences in chemical profiles between ZJT and YKT. A total of 66 major differential metabolites included catechins, proanthocyanins, flavonol and flavone glycosides, phenolic acids, amino acids and alkaloids were identified in ZJT. Among them, anthocyanins are the most characteristic metabolites. Nine glycosides of anthocyanins and six glycosides of proanthocyanins were found to be significantly higher in ZJT than that in YKT. Subsequently, pathway analysis revealed that ZJT might generate anthocyanins and proanthocyanins through the flavonol and flavone glycosides. Furthermore, quantitative analysis showed absolutely higher concentrations of total anthocyanins in ZJT, which correlated with the metabolomics results. This study presented the comprehensive chemical profiling and the characterized metabolites of ZJT. These results also provided chemical evidence for potential health functions of ZJT

Unexpected Hepatotoxicity in a Phase I Study of TAS266, a Novel Tetravalent Agonistic Monoclonal Nanobody® Targeting the DR5 Receptor

ABSTRACT Purpose: TAS266 is a novel agonistic tetravalent Nanobody® targeting the DR5 receptor. In preclinical studies, TAS266 was more potent than a cross-linked DR5 antibody or TRAIL. This first-in-human study was designed to evaluate the safety and tolerability, maximum tolerated dose, pharmacokinetics, pharmacodynamics, immunogenicity and preliminary efficacy of TAS266. Methods: Adult patients with advanced solid tumors were to receive assigned doses of TAS266 (3, 10, 15, or 20 mg/kg) intravenously on days 1, 8, 15, and 22 of a 28-day treatment cycle. Results: Grade ≥3 elevations in aspartate aminotransferase (AST) and/or alanine aminotransferase (ALT) levels, occurring during cycle 1 in 3 of 4 patients at the 3 mg/kg dose level, were attributed to TAS266 and led to early study termination. Liver enzyme levels quickly returned to grade ≤1 following TAS266 discontinuation. Evidence of pre-existing antibodies able to bind to TAS266 was found in the 3 patients who experienced these dose-limiting toxicities. Immunogenic responses remained elevated and strengthened at end-of-treatment (EOT). In the 1 patient who did not develop hepatotoxicity, no evidence of immunogenicity was observed at baseline or following administration of 4 TAS266 doses; however, incipient positive immunogenicity was observed at the EOT visit. Conclusion: TAS266 was associated with unexpected, significant but reversible hepatotoxicity. Although the underlying mechanism is not fully elucidated, factors including the molecule’s high potency, immunogenicity to TAS266 and possibly increased DR5 expression on hepatocytes further enhancing the activity of the Nanobody®, may have contributed to enhanced DR5 clustering and activation of hepatocyte apoptosis